Fault tolerant control aims at making control design able to anticipate to incipient and abrupt faults in the system under control in order to prevent these faults to turn into (partial) system failure. In this lecture, we present two methodologies for performing the fault detection (and possibly fault isolation) task. The first is the signal based analysis via nonparametric analysis techniques, such as power spectra, wavelets, residual analysis of adaptive filtering. These techniques enable the derivation of different particular signal features that can be used in a classification step to perform fault detection and isolation. A classification methods based on Bayesian Support Vector Machines will be presented and illustrated in health monitoring of a vibrating mechanical real life structure. The second is the model based analysis via state reconstruction techniques. Here a multiple model state reconstruction approach in discrete time is presented for detecting changes in the local model, so to enable the robust controller updating methodology presented in [1], to reconfigure the controller. A discrete multiple model state reconstruction methodology will be presented that can integrate fault information in continuous time models, derived from first principles modeling, with model information from multiple model identification methods.
[1 ] S. Kanev, Robust Fault Tolerant Control, Ph.D. dissertation, University of Twente,2004.

Short bio:
Michel Verhaegen received an engineering degree in aeronautics from the Delft University of Technology, The Netherlands, in August 1982, and the doctoral degree in applied sciences from the Catholic University Leuven, Belgium, in November 1985. During his graduate study, he held a research assistenship sponsored by the Flemish Institute for scientific research (IWT). From 1985 to 1994 he has been research fellow of the U.S. National Research Council (NRC), affiliated with the NASA Ames Research Center in California, and of the Dutch Academy of Arts and Sciences, affiliated with the Network Theory Group of the Delft University of Technology. In the period 1994-1999 he was an Associate Professor of the control laboratory of the Delft University of Technology and became appointed as full professor at the faculty of Applied Physics of the University of Twente in the Netherlands in 1999. From 2001 on Prof. Verhaegen moved back to the University of Delft and is now directing together with Prof. van den Hof the new Delft Center for Systems and Control. This center has brought together 3 research groups in Delft that focus on practically motivated fundamental research in the area of Systems and Control. The center, established in 2004, currently has 5 full time professors, 9 permanent staff members, 14 support staff members, about 40 Ph.D students and about 40 M.Sc students. His main research interest is the interdisciplinary domain of numerical algebra and system theory. In this field he has published over 100 papers. Current activities focus on the transfer of knowledge about new identification and controller design methodologies to industry. Application areas include mechatronic and microsystems, physical imaging and smart transportation systems.

Abstract of the Plenary Session: Innovative research challenges arising in gas turbine engine control

Gas turbine engine control systems have demanding requirements which are not necessarily synonymous with mainstream academic control research interests. For example, despite being a demanding nonlinear control problem, control laws for safe and reliable engines are well understood.
However, in the design of controllers for new engines, the economic cost of design and implementation is paramount. Thus, efficient design methods are important. Also, designs which address such non-traditional control specifications as weight, choice of actuators and sensors, and electrical connectivity can yield important commercial and environmental advantages. Further, in common with many industries, there is a recognition of the enormous value of data collected through the feedback process, not only for real-time control but also for optimisation and monitoring processes. An imaginative global computing environment is being explored which harnesses e-science and the computational grid for the support of engine reliability and maintenance. This talk will describe developments in all of these areas.

Short bio:
Peter Fleming is Chair of Industrial Systems and Control in the Department of Automatic Control and Systems Engineering and Director of the Rolls-Royce University Technology Centre for Control and Systems Engineering at the University of Sheffield, UK. He is currently Pro-Vice-Chancellor for External Relations at the University of Sheffield, committed to building relationships that ensure that the extensive knowledge base of the University is exploited to the benefit of the wider community, across business, commerce and public sector activities locally, nationally and internationally. His control and systems engineering research interests include multi-criteria decision-making, optimisation and scheduling, grid computing, software for control system design and implementation, and real-time control and instrumentation. These interests have led to the development of close links with a variety of industries in sectors such as aerospace, power generation, food processing, pharmaceuticals and manufacturing. He is a Fellow of the Royal Academy of Engineering, a Fellow of the Institution of Electrical Engineers and of the Institute of Measurement and Control, Vice-President of the International Federation of Automatic Control (IFAC) and is Editor-in-Chief of International Journal of Systems Science.

Abstract of the Plenary Session: Nonlinear System Identification using Kernels and Information Theoretic Learning

This talk will address the applications to nonlinear system identification and control of Kernel Methods and of Information Theoretic Learning. These two topics will be shown very much related. A new similiarity metric called correntropy will be shown to define a new RKHS that seems very appropriate for non-gaussian, nonlinear signal modeling.

Short bio:
Jose C. Principe is Distinguished Professor of Electrical and Biomedical Engineering at the University of Florida since 2002. He joined the University of Florida in 1987, after an eight year appointment as Professor at the University of Aveiro, in Portugal. Dr. Principe holds degrees in electrical engineering from the University of Porto (Bachelor), Portugal, University of Florida (Master and Ph.D.), USA and a Laurea Honoris Causa degree from the Universita Mediterranea in Reggio Calabria, Italy. Dr. Príncipe interests lie in nonlinear non-Gaussian optimal signal processing and modeling and in biomedical engineering. He created in 1991 the Computational NeuroEngineering Laboratory to synergistically focus the research in biological information processing models. Dr. Principe is a Fellow of the IEEE, past President of the International Neural Network Society, and Editor in Chief of the Transactions of Biomedical Engineering since 2001, as well as a former member of the Advisory Science Board of the FDA. He holds 5 patents and has submitted seven more. Dr. Principe has been supervisory committee chair of 47 Ph.D. and 61 Master students, and he is author of more than 400 refereed publications (3 books, 4 edited books, 14 book chapters, 116 journal papers and 276 conference proceedings).

Abstract of the Plenary Session: Optimization-based Control of Hybrid Dynamical Systems

Hybrid systems are heterogeneous systems that exhibit both continuous dynamics (difference or differential equations) and discrete dynamics (automata, logic transitions and switching, piecewise linear mappings, quantized commands, etc.). In this talk we introduce a modeling framework for hybrid systems, either based on discrete-time or on discrete-events, that is directly tailored to the synthesis of stabilizing model predictive controllers based on combinatorial optimization and multiparametric programming. We show that hybrid optimal control laws can be computed in closed-form and that they are piecewise affine state-feedback functions, a very attractive feature for fast-sampling applications. We also propose the combined use of convex programming and constraint satisfaction techniques as an efficient approach to solve complex optimal control problems for hybrid systems. The proposed techniques will be exemplified on a few industrial case studies.

Short bio:
Alberto Bemporad received the master degree in Electrical Engineering in 1993 and the Ph.D. in Control Engineering in 1997 from the University of Florence, Italy. He spent the academic year 1996/97 at the Center for Robotics and Automation, Dept. Systems Science & Mathematics, Washington University, St. Louis, as a visiting researcher. In 1997-1999, he held a postdoctoral position at the Automatic Control Lab, ETH, Zurich, Switzerland, where he collaborated as a senior researcher in 2000-2002. Since 1999 he is with the Faculty of Engineering of the University of Siena, Italy, where he is currently an Associate professor. He has published several papers in the area of hybrid systems, model predictive control, multiparametric optimization, computational geometry, robotics, and automotive control. He is coauthor of the Model Predictive Control Toolbox (The Mathworks, Inc.) and author of the Hybrid Toolbox for Matlab. He was an Associate Editor of the IEEE Transactions on Automatic Control during 2001-2004. He is Chair of the Technical Committee on Hybrid Systems of the IEEE Control Systems Society since 2002.

Abstract of the Plenary Session: Computer and Robot Vision: Learning from Biology

Building artificial vision systems has been a challenging research issue in the past decades. As a consequence of such research effort, considerable knowledge has been created in different topics, from multi-view geometry, physics-based vision, object recognition and motion analysis, etc. This progress has been grounded on the use of solid tools for the analysis, modeling and synthesis of vision systems, together with a massive growth of the available computational power and memory. In spite of this, most artificial vision systems still lack the generality, plasticity, robustness and apparent simplicity that we can find in their biological counterparts. It is therefore important to learn from biology how many living beings use vision to solve numerous tasks in such an efficient manner. By modeling and analyzing some of the principles of biological vision systems, we can find new ways of designing better artificial systems. In addition, by building artificial systems based on such principles, we can better understand natural vision systems and, ultimately, validate new theories on visual perception or cognition.
This lecture addresses some problems in computer and robot vision, while drawing the attention towards solutions found in biology. It thus raises questions that have recently drawn substantial attention for researchers. What cameras should be used? What geometry and resolution? How should a mobile system use vision to acquire and represent internal models of its environment for navigation? How can an artificial system track moving objects or learn how to manipulate them? For all these tasks, either purely visual or visuo-motor, we can extract important lessons from biology vision systems. Throughout this process, we can pursue new approaches for the design of artificial vision systems while, at the same time, contribute to a better understanding of biological systems. As a final word, this lecture stresses the importance of a multidisciplinary perspective towards building artificial vision systems. This is also valid for other engineering domains and a truly multidisciplinary approach has been the rationale inspiring several international research programmes, launched in the past few years.

Short bio:
José Santos-Victor received the PhD from IST in 1995. He is currently an Associate Professor at IST, and a researcher at the Instituto de Sistemas e Robotica (ISR), in the Computer Vision Lab (VisLab). His main research interests are in the area of vision based control and navigation, with emphasis on the relationship between visual perception and action, both in biological and artificial systems (including humanoids robots). He was the principal investigator in several international R&D projects in the areas of Computer Vision and Robotics (Esprit LTR Project 30185-NARVAL, EU FET-1999-29017-Omniviews, Esprit LTR-Proj. 21894-VIRSBS, TMR Network FMRX-CT96-0052 SMARTII, and IST-2000-28159 Project MIRROR, CAVIAR and ROBOTCUB). He is a member of the program committee of various international conferences on computer vision and robotics. He is an associate Editor of the IEEE Transactions on Robotics.

Abstract of the Plenary Session: Control Lessons Learned during the Cassini / Huygens - Mission to Explore the Saturnian Moon Titan

In July 2004, after a travel of 7 years the Cassini/Huygens-spacecraft of NASA/ESA arrived at Saturn, and delivers since then interesting images and measurements from Saturn, his rings and his more than 30 moons. At 14. January 2005 the descent probe "Huygens", built in European industry, entered the atmosphere of Titan, the largest moon of Saturn, and explored very successfully this amazing atmosphere, in which the existence of organic molecules has been proven earlier. The seminar presents challenging technology approaches to enable this mission, but also recent fascinating images of this remote, bizarre world of Titan: with rivers and lakes made of Methane. The atmosphere of Titan was only poorly known before; therefore in particular the descent of the Huygens Probe raised interesting technology challenges to autonomous reaction capabilities. As the signals require 67 Minutes to transfer the distance between Titan and Earth, several versions of the on-board data processing systems were considered to autonomously control the parachute descent system to land in time on the surface of Titan. Technology spin offs will be presented related to adaptive Mars Rover control, but also to robots for terrestrial emergency support teams.

Short bio:
Prof. Dr. Klaus Schilling worked in space industry on the development of interplanetary satellites and vehicles. In particular he had responsibility in the system design of the Huygens Probe and contributed to the development of the autonomous descent control system. Today he is chair for Robotics and Telematics at the Julius-Maximilian-University Würzburg; He was Chairman of the Technical Committee on Aerospace of the International Federation on Automatic Control (IFAC) and is Consulting Professor at Stanford University, Department of Aeronautics and Astronautics.